Which of the following sources is going to produce big data the fastest? The answer is none of them. Big data has been around for a long time. But it has not yet been used successfully in business operations and there are many reasons why this is so. The most important reason is that it is really expensive to store, manage and analyze this data. In addition, we still do not have a decent knowledge about how to properly use this new data.
But as data storage and analysis become cheaper and more affordable over the coming years, more businesses will start using them. In particular, data mining is a very new area of activity that came from NASA (National Space Transportation Safety Administration). This technique makes use of previously stored data in order to make predictions and to collect actionable intelligence. In order to do this it makes use of both humans and complex machinery. The human element is based on asking people in various industries (such as stock traders) who they think have information regarding product trends, manufacturing improvements or customer satisfaction.
Secondly, data scientists make use of sophisticated analytical algorithms in order to build big data warehouses, and they also use their mathematical skills in order to mine the massive amounts of data that is out there. Finally, large teams of these analysts work on the analytics part of their projects. It takes years of experience for one to become adept at these skills and in order to be able to bring up a winning formula for their project, these experts need plenty of fresh data sets to base their work on. However, if the team does not already have a working formula, it may take years to develop one.
Large databases are needed to run the predictive analytics algorithms which these specialists need to create formulas that take into account past sales, future predictions, current trends and the amount of processing time which would be required for each of these factors. These algorithms then need to be able to crunch through all the figures and come up with predictions. In order to make these calculations and predictions possible, the large data elements have to be processed quickly so that they can be released to the appropriate analytics teams. However, it is not enough just to process raw data – it needs to be processed right.
The quickest way for which of the following sources is likely to produce big data is via a two-page ref counting index. First of all, a large amount of data warehouses have been built using very large and complex spreadsheet tools which take a long time to process. However, even the simplest analytics systems do not always work, especially when it comes to large quantities of data warehouses. If a company has a database which contains millions upon millions of customer records, then it may take hundreds or even thousands of servers to be able to process this data. In the worst case scenario, an analytics team may simply be too costly to hire.
So where does this leave the question of which of the following sources is likely to produce big data quickly? Well, it does leave the issue open to a solution which is much more affordable than acquiring a majority of new servers or purchasing hundreds of new servers. This solution comes in the form of Perpetual Data Stream Mining or PDSM for short. With this method, the company simply has to buy access to a continuous source of real time data, which it can process in accordance with its algorithms at whatever point of time it deems necessary. Thus, rather than wait for its algorithms to be able to cope with the latest trends in the market, it will immediately work on finding fresh and relevant sources of real time data, which it can use to process its own data stream queries.
Of course, answering the question which of the following sources is likely to produce big data the fastest will be easier said than done. There are several companies today, which specialize in providing data streaming analytics services which can help even the smallest organizations to analyze large and complex historical data streams which have been analyzed over the last several years. But the question remains as to which of the two kinds of methods should one go for. If you have the cash to burn, then Perpetual Data Stream Mining would be your best bet. However, if you have limited budget, then you might want to consider Perpetual Data Stream Monitoring as your answer.
For any businesses, getting the answer which of the following sources is likely to produce big data the fastest could mean the difference between growing and dying out in the industry. The first thing that you need to do is ask yourself whether or not you are willing to invest the necessary money that goes into buying and setting up new servers to run this kind of software. Also, do you have the manpower which would enable you to maintain and tweak your existing systems for running such a program? If not, then you might want to turn to the next option available which is called MapReduce.